PROJECT SUMMARY Technology for recording from the brain is developing at a breakneck pace. But the digital integration of data acquired from different recording technologies is an impediment to the rapid adoption of these technologies across labs, and also makes analysis by interested 3rd parties, such as theorists, difficult. This lack of integration is a major barrier to scientific inquiry, as labs cannot easily analyze each other's data. Funding agencies are increasingly concerned with reproducibility, rigor, and distribution of data, even though there is no generally accepted ?library? process for sharing these data. Common data interfaces would facilitate the development of common analysis code, leading to an increase in code testing and robustness, and an increase in reproducibility and rigor. In this proposal, a data interface standard for neurophysiological and imaging data is developed. The standard is not a file format but rather is a systematic means of specifying and accessing data used in the neurosciences, including voltage waveforms, imaging data, spike times of neurons, and intensity values in regions-of-interest within imaging data. Versions are developed in Matlab and Python, but the data interface standard can be written in any programming language. The proposal includes the development of file readers for several Multifunction Data Acquisition Devices, 2-photon microscopes, stimulus devices, and ?apps? for imaging, spike, and stimulus analysis. The ease and power of the interface is tested in multiple data access events involving graduate students and postdoctoral researchers in neuroscience. Two of these sessions are reviewed by an outside group with expertise in interface design and human factors. The standard is revised after each session to improve ease and power. The proposal also includes the conversion of several data sets from a variety of systems neuroscientists and modelers, including BRAIN researchers. These data sets include neural recordings, laboratory stimuli, and behavioral data, and range from long-duration recordings of central pattern generators, to long-duration recordings of cortical neurons, to studies of navigation and taste perception. Conversion of these data sets will force the development team to ensure that these data can be specified easily, and also provide a first group of data sets for other scientists or amateurs to analyze. The long range goal is to enable experimentalists, theorists, and even amateurs to exchange data easily and to begin meaningful analysis within the hour of download. This has the capability to transform neuroscience into a discipline more like astronomy, where data is widely shared and many theorists and amateurs contribute to new discoveries.